Papers with Code 아카이브
Pose Estimation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 17
- 평가표
- 31
- 보존된 평가 기록
- 235
Pose Estimation is a computer vision task where the goal is to detect the position and orientation of a person or an object. Usually, this is done by predicting the location of specific keypoints like hands, head, elbows, etc. in case of Human Pose Estimation.
A common benchmark for this task is MPII Human Pose
<span style="color:grey; opacity: 0.6">( Image credit: Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/jesbu1/carl
Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement Learning
https://github.com/jgraving/DeepPoseKit
https://github.com/jhihan/rsna_pneumonia_detection
https://github.com/jiajunhua/facebookresearch-DensePose
https://github.com/jiajunhua/facebookresearch-Detectron
https://github.com/jiang-du/BlazePose-tensorflow
https://github.com/jiang-hb/diffusionreg
SE(3) Diffusion Model-based Point Cloud Registration for Robust 6D Object Pose Estimation
https://github.com/jiangzhongkai/ifly-algorithm_challenge
https://github.com/jianing-sun/Mask-YOLO
https://github.com/jiean001/models_m/tree/main/DecoMR
https://github.com/jimmycv07/DiffIR2VR-Zero
Upscale-A-Video: Temporal-Consistent Diffusion Model for Real-World Video Super-Resolution
https://github.com/jingpeilu/keypoint_optimization
Pose Estimation for Robot Manipulators via Keypoint Optimization and Sim-to-Real Transfer
https://github.com/jingpeilu/psmnet_ros
https://github.com/jixiaozhong/RealSR
https://github.com/jklife3/maskrcnn-impl
https://github.com/jkvt2/Latent-Pose-Descriptors
https://github.com/jlanday/Language-Detection
https://github.com/jlanday/Model-Selection-for-Pion-Photoproduction
https://github.com/jlanday/Towards-the-Minimal-Spectrum-of-Excited-Baryons
https://github.com/jlanday/X-ray-Object-Classification
https://github.com/jodumagpi/Xray-ObjSep-v1
https://github.com/joezengcbs/mini_xgboost_from_scratch
https://github.com/johnhw/simple_neural_ode
https://github.com/jokerjohn/cloud_map_evaluation
https://github.com/jomariya23156/SuperGlue-for-Visual-Place-Recognition
SuperGlue: Learning Feature Matching with Graph Neural Networks
https://github.com/jooyounghun/AI-Team-5
https://github.com/jordiae/DeepLearning-MAI
https://github.com/jp-sm/jp-sm.github.io
https://github.com/jpviguerasguillen/feedback-non-local-attention-fnla
https://github.com/jpviguerasguillen/feedback-non-local-attention_fNLA
https://github.com/jrbotvinick/learning-dynamics-on-invariant-measures
https://github.com/jreisam/Unity-OpenPose-Edutable
https://github.com/jremillard/images-to-osm
https://github.com/jshermeyer/VDSR4Geo
Accurate Image Super-Resolution Using Very Deep Convolutional Networks
https://github.com/jsll/pytorch_6dof-graspnet
6-DOF GraspNet: Variational Grasp Generation for Object Manipulation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.